Cited by 5Membrane Separation TechnologiesProteins in Food SystemsProtein purification and stability

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Selective removal of green pigments and associated proteins from clover-grass protein concentrates: molecular insights into a non-destructive, two-stage membrane-based biorefinery concept for high-quality food protein production

Open access
Gregersen Echers, Simon, Abdul-Khalek, Naim, Jensen, Nete Hassing, Jørgensen, Anders Kjær, Mattsson, Tuve, Jørgensen, Mads Koustrup, Lübeck, Peter Stephensen, Lübeck, Mette·2026·Sustainable Food Technology

Published version of a preprint

Abstract

A two-stage membrane-based green biorefinery removes pigments (Chl + Cart) via selective retention of large complexes with specific proteins (CBPs and PI/II) thereby producing a RuBisCO- and antioxidant-rich concentrate suitable for food application.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1039/d5fb00771b

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 132

  1. Lynch, 2018, Nutrients, 10, 1841, 10.3390/nu10121841 · DOI
  2. Sadhukhan, 2020, Sustainability, 12, 10.3390/su12051976 · DOI
  3. Prasanna, 2025, Environ. Dev. Sustain., 27, 15113, 10.1007/s10668-024-04642-1 · DOI
  4. Sá, 2020, Trends Food Sci. Technol., 97, 170, 10.1016/j.tifs.2020.01.011 · DOI
  5. Sözer, 2015, Agro Food Ind. Hi-Tech, 26, 56
  6. Meltzer, 2024, Food Nutr. Res., 68, 10.29219/fnr.v68.10489 · DOI
  7. Odgaard, 2019, J. Cleaner Prod., 223, 917, 10.1016/j.jclepro.2019.03.072 · DOI
  8. Thomsen, 2025, Waste Biomass Valorization, 1
  9. S. K.Soni and R.Soni , Challenges and Opportunities in Green Biorefinery , Clean Energy Production Technologies , 2025 pp. 399–433 , https://link.springer.com/chapter/10.1007/978-981-96-9013-8_12 · DOI
  10. Gaffey, 2023, Biotechnol. Adv., 66, 108168, 10.1016/j.biotechadv.2023.108168 · DOI
  11. Ravindran, 2021, Clean Technol., 3, 656, 10.3390/cleantechnol3030039 · DOI
  12. Damborg, 2020, Anim. Feed Sci. Technol., 261, 114401, 10.1016/j.anifeedsci.2020.114401 · DOI
  13. Solati, 2018, Ind. Crops Prod., 115, 214, 10.1016/j.indcrop.2018.02.010 · DOI
  14. M.Humphreys , U.Feuerstein , M.Vandewalle and J.Baert , Ryegrasses , Fodder Crops and Amenity Grasses , 2010 , pp. 211–260 , https://link.springer.com/chapter/10.1007/978-1-4419-0760-8_10 · DOI
  15. Jørgensen, 2022, Grass Forage Sci., 77, 295, 10.1111/gfs.12594 · DOI
  16. Koshchuh, 2004, Desalination, 163, 253, 10.1016/S0011-9164(04)90197-X · DOI
  17. Shinali, 2024, Foods, 13, 1163, 10.3390/foods13081163 · DOI
  18. Andersson, 2008, Plant Physiol. Biochem., 46, 275, 10.1016/j.plaphy.2008.01.001 · DOI
  19. Lin, 2014, Nature, 513, 547, 10.1038/nature13776 · DOI
  20. Di Stefano, 2018, J. Am. Oil Chem. Soc., 95, 1063, 10.1002/aocs.12104 · DOI
  21. Ma, 2025, Food Hydrocoll., 160, 110820, 10.1016/j.foodhyd.2024.110820 · DOI
  22. Ma, 2025, Food Hydrocoll., 160, 110783, 10.1016/j.foodhyd.2024.110783 · DOI
  23. Zhou, 2022, Food Chem., 397, 133808, 10.1016/j.foodchem.2022.133808 · DOI
  24. Grácio, 2023, Food Chem., 419, 135993, 10.1016/j.foodchem.2023.135993 · DOI
  25. Di Stefano, 2018, J. Am. Oil Chem. Soc., 95, 1063, 10.1002/aocs.12104 · DOI
  26. Echeverria-Jaramillo, 2025, Future Foods, 12, 100740, 10.1016/j.fufo.2025.100740 · DOI
  27. Wold, 2005, Int. Dairy J., 15, 343, 10.1016/j.idairyj.2004.08.009 · DOI
  28. Suman, 2008, J. Sci. Food Agric., 88, 471, 10.1002/jsfa.3109 · DOI
  29. Meléndez-Martínez, 2022, Crit. Rev. Food Sci. Nutr., 62, 1999, 10.1080/10408398.2020.1867959 · DOI
  30. Adadi, 2018, J. Agric. Food Chem., 66, 5925, 10.1021/acs.jafc.8b01407 · DOI
  31. Anoop, 2023, Compr. Rev. Food Sci. Food Saf., 22, 473, 10.1111/1541-4337.13079 · DOI
  32. Chen, 2022, npj Sci. Food, 6, 1
  33. Wan, 2024, Food Hydrocoll., 149, 109540, 10.1016/j.foodhyd.2023.109540 · DOI
  34. Ravindran, 2021, Clean Technol., 3, 656, 10.3390/cleantechnol3030039 · DOI
  35. Andrade, 2022, Comput.-Aided Chem. Eng., 51, 877, 10.1016/B978-0-323-95879-0.50147-8 · DOI
  36. Santamaría-Fernández, 2020, Anim. Feed Sci. Technol., 268, 114605, 10.1016/j.anifeedsci.2020.114605 · DOI
  37. Santamaria-Fernandez, 2019, Biofuels, Bioprod. Biorefin., 13, 502, 10.1002/bbb.1957 · DOI
  38. Thornton, 2023, Curr. Opin. Environ. Sustain., 62, 101277, 10.1016/j.cosust.2023.101277 · DOI
  39. Møller, 2021, J. Agric. Food Chem., 69, 14341, 10.1021/acs.jafc.1c04289 · DOI
  40. Nynäs, 2021, Foods, 10, 2533, 10.3390/foods10112533 · DOI
  41. Delahaije, 2022, Colloids Surf., A, 646, 128950, 10.1016/j.colsurfa.2022.128950 · DOI
  42. Furia, 2025, Innovative Food Sci. Emerging Technol., 103, 104037, 10.1016/j.ifset.2025.104037 · DOI
  43. Martin, 2019, J. Sci. Food Agric., 99, 1568, 10.1002/jsfa.9335 · DOI
  44. Tanambell, 2023, Innovative Food Sci. Emerging Technol., 87, 103408, 10.1016/j.ifset.2023.103408 · DOI
  45. Nieuwland, 2021, ACS Food Sci. Technol., 1, 908, 10.1021/acsfoodscitech.1c00009 · DOI
  46. Gifuni, 2020, Algal Res., 51, 102082, 10.1016/j.algal.2020.102082 · DOI
  47. Corona, 2018, Sci. Total Environ., 635, 100, 10.1016/j.scitotenv.2018.03.357 · DOI
  48. Møller, 2021, J. Agric. Food Chem., 69, 14341, 10.1021/acs.jafc.1c04289 · DOI
  49. Bazzarelli, 2022, Membranes, 12, 585, 10.3390/membranes12060585 · DOI
  50. Nynäs, 2024, ACS Food Sci. Technol., 4, 126, 10.1021/acsfoodscitech.3c00426 · DOI
  51. Mattsson, 2025, Future Foods, 100846, 10.1016/j.fufo.2025.100846 · DOI
  52. Echeverria-Jaramillo, 2025, Future Foods, 12, 100740, 10.1016/j.fufo.2025.100740 · DOI
  53. Li, 2018, J. Dairy Sci., 101, 8954, 10.3168/jds.2018-14763 · DOI
  54. Yu, 2024, Food Hydrocoll., 155, 110245, 10.1016/j.foodhyd.2024.110245 · DOI
  55. Keppler, 2020, Trends Food Sci. Technol., 101, 38, 10.1016/j.tifs.2020.04.023 · DOI
  56. Schneider, 2016, Food Funct., 7, 1306, 10.1039/C5FO01499A · DOI
  57. Dai, 2024, Food Hydrocoll., 146, 109197, 10.1016/j.foodhyd.2023.109197 · DOI
  58. Amer, 2021, Int. J. Food Sci. Technol., 56, 302, 10.1111/ijfs.14632 · DOI
  59. Danner Aakjaer Pedersen, 2025, Proteomes, 13, 8, 10.3390/proteomes13010008 · DOI
  60. Incamps, 2005, Biotechnol. Bioeng., 91, 447, 10.1002/bit.20520 · DOI
  61. Pérez-Vila, 2025, Food Chem., 468, 142411, 10.1016/j.foodchem.2024.142411 · DOI
  62. Liu, 2025, Front. Genet., 16, 1600377, 10.3389/fgene.2025.1600377 · DOI
  63. Wilson, 2002, Proteomics, 2, 1114, 10.1002/1615-9861(200209)2:9<1114::AID-PROT1114>3.0.CO;2-O · DOI
  64. Bertrand, 2016, BMC Plant Biol., 16, 1, 10.1186/s12870-016-0751-2 · DOI
  65. Yu, 2019, J. Proteome Res., 18, 2446, 10.1021/acs.jproteome.8b00951 · DOI
  66. Hou, 2019, Environ. Exp. Bot., 162, 95, 10.1016/j.envexpbot.2019.02.002 · DOI
  67. Peng, 2019, Agronomy, 9, 843, 10.3390/agronomy9120843 · DOI
  68. Xiong, 2017, Proteome Sci., 15, 1, 10.1186/s12953-017-0127-z · DOI
  69. Li, 2013, PLoS One, 8, e82725, 10.1371/journal.pone.0082725 · DOI
  70. Goktayoglu, 2023, J. Agric. Food Chem., 71, 9157, 10.1021/acs.jafc.2c09190 · DOI
  71. Aiello, 2020, Molecules, 25, 1485, 10.3390/molecules25071485 · DOI
  72. Fasoli, 2011, J. Proteomics, 74, 127, 10.1016/j.jprot.2010.10.009 · DOI
  73. H. K.Lichtenthaler and C.Buschmann , Chlorophylls and Carotenoids: Measurement and Characterization by UV-VIS Spectroscopy , Current Protocols in Food Analytical Chemistry , 2001 , vol. 1 , 1 , pp. F431–F438 , https://onlinelibrary.wiley.com/doi/full/10.1002/0471142913.faf0403s01 · DOI · Paper page
  74. H. K.Lichtenthaler and C.Buschmann , Extraction of Phtosynthetic Tissues:Chlorophylls and Carotenoids , Current Protocols in Food Analytical Chemistry , 2001 , vol. 1 , 1 , pp. F421–F426 , https://onlinelibrary.wiley.com/doi/full/10.1002/0471142913.faf0402s01 · DOI
  75. Gregersen, 2025, Innovative Food Sci. Emerging Technol., 106, 104277, 10.1016/j.ifset.2025.104277 · DOI
  76. Tyanova, 2016, Nat. Protoc., 11, 2301, 10.1038/nprot.2016.136 · DOI
  77. Cox, 2008, Nat. Biotechnol., 26, 1367, 10.1038/nbt.1511 · DOI · Paper page
  78. Larsen, 2019, Ind. Crops Prod., 130, 642, 10.1016/j.indcrop.2019.01.016 · DOI
  79. The UniProt Consortium , UniProt: the universal protein knowledgebase , Nucleic Acids Res. , 2017 45 ( D1 ), D158D169
  80. Cox, 2014, Mol. Cell. Proteomics, 13, 2513, 10.1074/mcp.M113.031591 · DOI · Paper page
  81. Perez-Riverol, 2022, Nucleic Acids Res., 50, D543, 10.1093/nar/gkab1038 · DOI · Paper page
  82. Quaglieri, 2022, bioRxiv, 517480
  83. A. J. J.Almagro , C. K.Sønderby , S. K.Sønderby , H.Nielsen and O.Winther , DeepLoc: prediction of protein subcellular localization using deep learning , in Bioinformatics , ed. Hancock J. , 2017 , vol. 33 , 21 , pp. 3387–3395 , http://www.cbs.dtu.dk/services/DeepLoc.Examplecodeisavailableathttps://github.com/JJAlmagro/subcellular_localization.Thedatasetisavailableathttp://www.cbs.dtu.dk/services/DeepLoc/data.php
  84. Tsirigos, 2015, Nucleic Acids Res., 43, W401, 10.1093/nar/gkv485 · DOI
  85. Gregersen Echers, 2022, Future Foods, 6, 100189, 10.1016/j.fufo.2022.100189 · DOI
  86. Pedregosa, 2012, J. Mach. Learn. Res., 12, 2825
  87. Cock, 2009, Bioinformatics, 25, 1422, 10.1093/bioinformatics/btp163 · DOI · Paper page
  88. W.McKinney , Data Structures for Statistical Computing in Python , SciPy Proceedings , 2010 , pp. 56–61 , https://proceedings.scipy.org/articles/Majora-92bf1922-00a · DOI
  89. Hunter, 2007, Comput. Sci. Eng., 9, 90, 10.1109/MCSE.2007.55 · DOI · Paper page
  90. Waskom, 2021, J. Open Source Softw., 6, 3021, 10.21105/joss.03021 · DOI · Paper page
  91. Virtanen, 2020, Nat. Methods, 17, 261, 10.1038/s41592-019-0686-2 · DOI · Paper page
  92. Harris, 2020, Nature, 585, 357, 10.1038/s41586-020-2649-2 · DOI · Paper page
  93. Lobry, 1994, Nucleic Acids Res., 22, 3174, 10.1093/nar/22.15.3174 · DOI
  94. Guruprasad, 1990, Protein Eng., 4, 155, 10.1093/protein/4.2.155 · DOI
  95. Kyte, 1982, J. Mol. Biol., 157, 105, 10.1016/0022-2836(82)90515-0 · DOI
  96. Gregersen, 2021, Food Chem.: X, 12, 100137
  97. Gregersen, 2022, Algal Res., 62, 102619, 10.1016/j.algal.2021.102619 · DOI · Paper page
  98. Mariotti, 2008, Crit. Rev. Food Sci. Nutr., 48, 177, 10.1080/10408390701279749 · DOI
  99. Zhang, Botany, 98, 137, 10.1139/cjb-2019-0082 · DOI
  100. Yeoh, 1994, Food Chem., 49, 245, 10.1016/0308-8146(94)90167-8 · DOI
  101. Hansen, 2022, Foods, 11, 3229, 10.3390/foods11203229 · DOI
  102. Tamayo Tenorio, 2016, Food Chem., 203, 402, 10.1016/j.foodchem.2016.02.092 · DOI
  103. la Cour, 2019, Front. Sustain. Food Syst., 3, 503165, 10.3389/fsufs.2019.00112 · DOI
  104. Inostroza-Blancheteau, 2017, Plant Physiol. Biochem., 113, 89, 10.1016/j.plaphy.2017.02.003 · DOI
  105. Nikbakht, 2014, Agron. J., 106, 585, 10.2134/agronj2013.0275 · DOI
  106. Acuña, 2021, Crop Sci., 61, 3775, 10.1002/csc2.20509 · DOI
  107. A.Solovchenko . Screening Pigments: General Questions . 2010 ; 931 , https://link.springer.com/chapter/10.1007/978-3-642-13887-4_2 · DOI
  108. Su, 2021, Ultrason. Sonochem., 76, 105653, 10.1016/j.ultsonch.2021.105653 · DOI
  109. Pathak, 2022, Eur. Biophys. J., 51, 335, 10.1007/s00249-022-01601-4 · DOI
  110. Justino, 2023, Int. J. Food Sci. Technol., 59, 498, 10.1111/ijfs.16673 · DOI
  111. Abdul-Khalek, 2023, Comput. Struct. Biotechnol. J., 21, 3715, 10.1016/j.csbj.2023.07.027 · DOI
  112. Abdul-Khalek, 2025, J. Proteome Res.
  113. Thumuluri, 2022, Nucleic Acids Res., 50, W228, 10.1093/nar/gkac278 · DOI
  114. Denancé, 2014, Plant Cell Physiol., 55, 469, 10.1093/pcp/pct198 · DOI
  115. Daniel, 2017, Arch. Biochem. Biophys., 632, 88, 10.1016/j.abb.2017.06.023 · DOI
  116. Jansson, 1994, Biochim. Biophys. Acta Bioenerg., 1184, 1, 10.1016/0005-2728(94)90148-1 · DOI
  117. Knoppová, 2022, Plant Physiol., 189, 790, 10.1093/plphys/kiac045 · DOI
  118. Bassi, 1993, Eur. J. Biochem., 212, 297, 10.1111/j.1432-1033.1993.tb17662.x · DOI
  119. Mattoo, 1999, Physiol. Plant., 107, 454, 10.1034/j.1399-3054.1999.100412.x · DOI
  120. Bhowal, 2020, BMC Genom., 21, 1, 10.1186/s12864-020-6547-7 · DOI
  121. Decker, 1998, Trends Food Sci. Technol., 9, 241, 10.1016/S0924-2244(98)00045-4 · DOI
  122. Pal, 2024, Plant Physiol. Biochem., 212, 108789, 10.1016/j.plaphy.2024.108789 · DOI
  123. Wu, 2004, J. Nutr., 134, 489, 10.1093/jn/134.3.489 · DOI · Paper page
  124. BASSI, 1993, Eur. J. Biochem., 212, 297, 10.1111/j.1432-1033.1993.tb17662.x · DOI
  125. Li, 2000, Nature, 403, 391, 10.1038/35000131 · DOI
  126. Król, 1995, Plant Physiol., 107, 873, 10.1104/pp.107.3.873 · DOI
  127. Kiraga, 2007, BMC Genom., 8, 1, 10.1186/1471-2164-8-163 · DOI
  128. Vinothkumar, 2010, Q. Rev. Biophys., 43, 65, 10.1017/S0033583510000041 · DOI
  129. Hedin, 2011, J. Proteome Res., 10, 3324, 10.1021/pr200145a · DOI
  130. Zhang, 2023, Food Hydrocoll., 144, 109008, 10.1016/j.foodhyd.2023.109008 · DOI
  131. Smialowski, 2007, Bioinformatics, 23, 2536, 10.1093/bioinformatics/btl623 · DOI
  132. Hanton, 2005, Plant Cell, 17, 3081, 10.1105/tpc.105.034900 · DOI

Metadata

Authors
Gregersen Echers, S., Abdul-Khalek, N., Jensen, N. H., Jørgensen, A. K., Mattsson, T., Jørgensen, M. K., Lübeck, P. S., & Lübeck, M.
Affiliations
  • Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg, Denmark
  • Aalborg University (DK)
Venue
Sustainable Food Technology
Published
2026
Volume
4
Issue
2
Pages
1689-1713
ISSN
2753-8095 (ISSN-L)
OpenAlex
W7108069026
Cited by
5
References
2
Citations by year
2026: 5
FWCI
1.27
Type
journal article
Language
en
Open access
Locations
  • Sustainable Food Technology (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • VBN Forskningsportal (Aalborg Universitet) (submittedVersion, cc-by-nc)
  • VBN Forskningsportal (Aalborg Universitet) (submittedVersion, cc-by-nc-nd)
Funders
  • Danmarks Frie Forskningsfond (1127-00245B)
  • Grønt Udviklings- og Demonstrations Program (34009-20-1657)
Topics
  • Physical Sciences › Environmental Science › Water Science and Technology › Membrane Separation Technologies
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Proteins in Food Systems
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
Keywords
green pigments, chlorophyll, carotenoids, Rubisco, antioxidant proteins, membrane separation, selective retention, biorefinery

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Selective removal of green pigments and associated proteins from clover-grass protein concentrates: molecular insights into a non-destructive, two-stage membrane-based biorefinery concept for high-quality food protein production. https://app.cochat.ai/papers/10.1039/d5fb00771b
The paper
Gregersen Echers, S., Abdul-Khalek, N., Jensen, N. H., Jørgensen, A. K., Mattsson, T., Jørgensen, M. K., Lübeck, P. S., & Lübeck, M. (2026). Selective removal of green pigments and associated proteins from clover-grass protein concentrates: molecular insights into a non-destructive, two-stage membrane-based biorefinery concept for high-quality food protein production. Sustainable Food Technology. https://doi.org/10.1039/d5fb00771b